A short description of the post.
Since late December 2019, an outbreak of a novel coronavirus disease (COVID-19; previously known as 2019-nCoV) was reported in Wuhan, China, which had subsequently affected 210 countries worldwide. In general, COVID-19 is an acute resolved disease but it can also be deadly, with a 2% case fatality rate.
The COVID-19 pandemic in Indonesia is part of the ongoing worldwide pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus was confirmed to have reached Indonesia on March 2, 2020. It started with two cases in March. As of July 31 2021, there had been 3,409,658 cumulative confirmed cases of COVID-19 in Indonesia and 94,119 reported cumulative deaths. All cases were spread in 34 provinces in Indonesia. Among all the provinces, DKI Jakarta (Indonesian: Daerah Khusus Ibukota Jakarta and in English: Special Capital Region of Jakarta) contributed close to 24% of the cumulative confirmed cases.
Despite its compactness, the cumulative confirmed cases were not evenly distributed within DKI Jakarta as Figure below reveals at the district and sub-district levels. The question was where were the sub-districts with relatively higher number of confirmed cases and how they changed over time.
Goals: - Identify the sub-districts with higher number of confirmed cases than average - Visualize the change in COVID-19 infection from March 2020 to August 2021
Packages used: maptools, sf, raster, spatstat and tmap, tidyverse, readxl
packages = c('maptools', 'sf', 'raster','spatstat', 'tmap','tidyverse','readxl','xlsx')
for(p in packages){
if(!require(p,character.only = T)){
install.packages(p)
}
library(p,character.only = T)
}
Reading the datas
jakarta = st_read(dsn = "data/Geospatial",
layer = "BATAS_DESA_DESEMBER_2019_DUKCAPIL_DKI_JAKARTA")
Reading layer `BATAS_DESA_DESEMBER_2019_DUKCAPIL_DKI_JAKARTA' from data source `C:\JunLonggggg\junlong-is415\_posts\2021-09-06-take-home-exercise-1\data\Geospatial'
using driver `ESRI Shapefile'
Simple feature collection with 269 features and 161 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 106.3831 ymin: -6.370815 xmax: 106.9728 ymax: -5.184322
Geodetic CRS: WGS 84Check crs
st_crs(jakarta)
Coordinate Reference System:
User input: WGS 84
wkt:
GEOGCRS["WGS 84",
DATUM["World Geodetic System 1984",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
CS[ellipsoidal,2],
AXIS["latitude",north,
ORDER[1],
ANGLEUNIT["degree",0.0174532925199433]],
AXIS["longitude",east,
ORDER[2],
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4326]]Setting the projection to EPSG:23845
jakarta_sf23845 = st_transform(jakarta, 23845)
st_crs(jakarta_sf23845)
Coordinate Reference System:
User input: EPSG:23845
wkt:
PROJCRS["DGN95 / Indonesia TM-3 zone 54.1",
BASEGEOGCRS["DGN95",
DATUM["Datum Geodesi Nasional 1995",
ELLIPSOID["WGS 84",6378137,298.257223563,
LENGTHUNIT["metre",1]]],
PRIMEM["Greenwich",0,
ANGLEUNIT["degree",0.0174532925199433]],
ID["EPSG",4755]],
CONVERSION["Indonesia TM-3 zone 54.1",
METHOD["Transverse Mercator",
ID["EPSG",9807]],
PARAMETER["Latitude of natural origin",0,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8801]],
PARAMETER["Longitude of natural origin",139.5,
ANGLEUNIT["degree",0.0174532925199433],
ID["EPSG",8802]],
PARAMETER["Scale factor at natural origin",0.9999,
SCALEUNIT["unity",1],
ID["EPSG",8805]],
PARAMETER["False easting",200000,
LENGTHUNIT["metre",1],
ID["EPSG",8806]],
PARAMETER["False northing",1500000,
LENGTHUNIT["metre",1],
ID["EPSG",8807]]],
CS[Cartesian,2],
AXIS["easting (X)",east,
ORDER[1],
LENGTHUNIT["metre",1]],
AXIS["northing (Y)",north,
ORDER[2],
LENGTHUNIT["metre",1]],
USAGE[
SCOPE["Cadastre."],
AREA["Indonesia - onshore east of 138°E."],
BBOX[-9.19,138,-1.49,141.01]],
ID["EPSG",23845]]Geometry set for 269 features
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -3691981 ymin: 663887.8 xmax: -3606237 ymax: 815440.9
Projected CRS: DGN95 / Indonesia TM-3 zone 54.1
First 5 geometries:Peeking at the dataframe
glimpse(jakarta_sf23845)
Rows: 269
Columns: 162
$ OBJECT_ID <dbl> 25477, 25478, 25397, 25400, 25378, 25379, 25390, ~
$ KODE_DESA <chr> "3173031006", "3173031007", "3171031003", "317103~
$ DESA <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA ~
$ KODE <dbl> 317303, 317303, 317103, 317103, 310101, 310101, 3~
$ PROVINSI <chr> "DKI JAKARTA", "DKI JAKARTA", "DKI JAKARTA", "DKI~
$ KAB_KOTA <chr> "JAKARTA BARAT", "JAKARTA BARAT", "JAKARTA PUSAT"~
$ KECAMATAN <chr> "TAMAN SARI", "TAMAN SARI", "KEMAYORAN", "KEMAYOR~
$ DESA_KELUR <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA ~
$ JUMLAH_PEN <dbl> 21609, 9069, 29085, 41913, 6947, 7059, 15793, 589~
$ JUMLAH_KK <dbl> 7255, 3273, 9217, 13766, 2026, 2056, 5599, 1658, ~
$ LUAS_WILAY <dbl> 0.36, 0.37, 0.53, 0.97, 0.93, 0.95, 1.76, 1.14, 0~
$ KEPADATAN <dbl> 60504, 24527, 54465, 42993, 7497, 7401, 8971, 515~
$ PERPINDAHA <dbl> 102, 25, 131, 170, 17, 26, 58, 13, 113, 178, 13, ~
$ JUMLAH_MEN <dbl> 68, 52, 104, 151, 14, 32, 36, 10, 60, 92, 5, 83, ~
$ PERUBAHAN <dbl> 20464, 8724, 27497, 38323, 6853, 6993, 15006, 580~
$ WAJIB_KTP <dbl> 16027, 7375, 20926, 30264, 4775, 4812, 12559, 398~
$ SILAM <dbl> 15735, 1842, 26328, 36813, 6941, 7057, 7401, 5891~
$ KRISTEN <dbl> 2042, 2041, 1710, 3392, 6, 0, 3696, 0, 4058, 5130~
$ KHATOLIK <dbl> 927, 1460, 531, 1082, 0, 0, 1602, 0, 2100, 2575, ~
$ HINDU <dbl> 15, 9, 42, 127, 0, 0, 622, 0, 25, 27, 0, 9, 115, ~
$ BUDHA <dbl> 2888, 3716, 469, 495, 0, 2, 2462, 0, 4134, 4740, ~
$ KONGHUCU <dbl> 2, 1, 5, 1, 0, 0, 10, 0, 9, 10, 0, 4, 1, 1, 4, 0,~
$ KEPERCAYAA <dbl> 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 2, 0, 22, 0, 3, ~
$ PRIA <dbl> 11049, 4404, 14696, 21063, 3547, 3551, 7833, 2954~
$ WANITA <dbl> 10560, 4665, 14389, 20850, 3400, 3508, 7960, 2937~
$ BELUM_KAWI <dbl> 10193, 4240, 14022, 20336, 3366, 3334, 7578, 2836~
$ KAWIN <dbl> 10652, 4364, 13450, 19487, 3224, 3404, 7321, 2791~
$ CERAI_HIDU <dbl> 255, 136, 430, 523, 101, 80, 217, 44, 381, 476, 3~
$ CERAI_MATI <dbl> 509, 329, 1183, 1567, 256, 241, 677, 220, 1197, 9~
$ U0 <dbl> 1572, 438, 2232, 3092, 640, 648, 802, 585, 2220, ~
$ U5 <dbl> 1751, 545, 2515, 3657, 645, 684, 995, 588, 2687, ~
$ U10 <dbl> 1703, 524, 2461, 3501, 620, 630, 1016, 513, 2653,~
$ U15 <dbl> 1493, 521, 2318, 3486, 669, 671, 1106, 548, 2549,~
$ U20 <dbl> 1542, 543, 2113, 3098, 619, 609, 1081, 491, 2313,~
$ U25 <dbl> 1665, 628, 2170, 3024, 639, 582, 1002, 523, 2446,~
$ U30 <dbl> 1819, 691, 2363, 3188, 564, 592, 1236, 478, 2735,~
$ U35 <dbl> 1932, 782, 2595, 3662, 590, 572, 1422, 504, 3034,~
$ U40 <dbl> 1828, 675, 2371, 3507, 480, 486, 1200, 397, 2689,~
$ U45 <dbl> 1600, 607, 2250, 3391, 421, 457, 1163, 365, 2470,~
$ U50 <dbl> 1408, 619, 1779, 2696, 346, 369, 1099, 288, 2129,~
$ U55 <dbl> 1146, 602, 1379, 1909, 252, 318, 979, 235, 1843, ~
$ U60 <dbl> 836, 614, 1054, 1397, 197, 211, 880, 162, 1386, 1~
$ U65 <dbl> 587, 555, 654, 970, 122, 114, 747, 111, 958, 932,~
$ U70 <dbl> 312, 311, 411, 631, 69, 55, 488, 65, 554, 573, 38~
$ U75 <dbl> 415, 414, 420, 704, 74, 61, 577, 38, 717, 642, 37~
$ TIDAK_BELU <dbl> 3426, 1200, 4935, 7328, 1306, 1318, 2121, 973, 50~
$ BELUM_TAMA <dbl> 1964, 481, 2610, 3763, 730, 676, 1278, 732, 3241,~
$ TAMAT_SD <dbl> 2265, 655, 2346, 2950, 1518, 2054, 1169, 1266, 44~
$ SLTP <dbl> 3660, 1414, 3167, 5138, 906, 1357, 2236, 852, 585~
$ SLTA <dbl> 8463, 3734, 12172, 16320, 2040, 1380, 5993, 1570,~
$ DIPLOMA_I <dbl> 81, 23, 84, 179, 22, 15, 43, 36, 85, 83, 4, 63, 2~
$ DIPLOMA_II <dbl> 428, 273, 1121, 1718, 101, 59, 573, 97, 604, 740,~
$ DIPLOMA_IV <dbl> 1244, 1241, 2477, 4181, 314, 191, 2199, 357, 1582~
$ STRATA_II <dbl> 74, 46, 166, 315, 10, 8, 168, 8, 63, 92, 5, 174, ~
$ STRATA_III <dbl> 4, 2, 7, 21, 0, 1, 13, 0, 3, 9, 0, 16, 8, 7, 75, ~
$ BELUM_TIDA <dbl> 3927, 1388, 5335, 8105, 1788, 1627, 2676, 1129, 5~
$ APARATUR_P <dbl> 81, 10, 513, 931, 246, 75, 156, 160, 132, 79, 23,~
$ TENAGA_PEN <dbl> 70, 43, 288, 402, 130, 93, 81, 123, 123, 73, 45, ~
$ WIRASWASTA <dbl> 8974, 3832, 10662, 14925, 788, 728, 6145, 819, 12~
$ PERTANIAN <dbl> 1, 0, 1, 3, 2, 2, 1, 3, 2, 5, 1, 1, 0, 0, 2, 5, 2~
$ NELAYAN <dbl> 0, 0, 2, 0, 960, 1126, 1, 761, 1, 2, 673, 0, 0, 0~
$ AGAMA_DAN <dbl> 6, 6, 5, 40, 0, 0, 49, 2, 10, 11, 0, 54, 15, 16, ~
$ PELAJAR_MA <dbl> 4018, 1701, 6214, 9068, 1342, 1576, 3135, 1501, 6~
$ TENAGA_KES <dbl> 28, 29, 80, 142, 34, 26, 60, 11, 48, 55, 16, 68, ~
$ PENSIUNAN <dbl> 57, 50, 276, 498, 20, 7, 59, 14, 56, 75, 2, 97, 5~
$ LAINNYA <dbl> 4447, 2010, 5709, 7799, 1637, 1799, 3430, 1368, 7~
$ GENERATED <chr> "30 Juni 2019", "30 Juni 2019", "30 Juni 2019", "~
$ KODE_DES_1 <chr> "3173031006", "3173031007", "3171031003", "317103~
$ BELUM_ <dbl> 3099, 1032, 4830, 7355, 1663, 1704, 2390, 1213, 5~
$ MENGUR_ <dbl> 4447, 2026, 5692, 7692, 1576, 1731, 3500, 1323, 7~
$ PELAJAR_ <dbl> 3254, 1506, 6429, 8957, 1476, 1469, 3185, 1223, 6~
$ PENSIUNA_1 <dbl> 80, 65, 322, 603, 24, 8, 70, 20, 75, 97, 2, 132, ~
$ PEGAWAI_ <dbl> 48, 5, 366, 612, 223, 72, 65, 143, 73, 48, 15, 89~
$ TENTARA <dbl> 4, 0, 41, 57, 3, 0, 74, 1, 20, 12, 2, 11, 90, 340~
$ KEPOLISIAN <dbl> 10, 1, 16, 42, 11, 8, 2, 9, 17, 7, 3, 9, 165, 15,~
$ PERDAG_ <dbl> 31, 5, 1, 3, 6, 1, 2, 4, 3, 1, 4, 0, 1, 2, 9, 2, ~
$ PETANI <dbl> 0, 0, 1, 2, 0, 1, 1, 0, 1, 1, 1, 2, 0, 0, 1, 2, 0~
$ PETERN_ <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ NELAYAN_1 <dbl> 1, 0, 1, 0, 914, 1071, 0, 794, 0, 1, 663, 0, 0, 0~
$ INDUSTR_ <dbl> 7, 3, 4, 3, 1, 3, 0, 0, 1, 7, 0, 0, 2, 2, 1, 3, 1~
$ KONSTR_ <dbl> 3, 0, 2, 6, 3, 8, 1, 6, 1, 5, 10, 0, 2, 5, 7, 4, ~
$ TRANSP_ <dbl> 2, 0, 7, 4, 0, 0, 0, 0, 0, 3, 0, 0, 0, 0, 6, 3, 2~
$ KARYAW_ <dbl> 6735, 3034, 7347, 10185, 237, 264, 4319, 184, 940~
$ KARYAW1 <dbl> 9, 2, 74, 231, 4, 0, 16, 1, 13, 10, 1, 24, 17, 29~
$ KARYAW1_1 <dbl> 0, 0, 5, 15, 0, 0, 0, 1, 0, 1, 0, 0, 2, 4, 7, 9, ~
$ KARYAW1_12 <dbl> 23, 4, 25, 35, 141, 50, 16, 157, 6, 9, 40, 11, 11~
$ BURUH <dbl> 515, 155, 971, 636, 63, 218, 265, 55, 1085, 652, ~
$ BURUH_ <dbl> 1, 0, 0, 0, 2, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 2, 1~
$ BURUH1 <dbl> 0, 0, 1, 0, 1, 25, 0, 2, 0, 1, 1, 0, 0, 0, 0, 0, ~
$ BURUH1_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PEMBANT_ <dbl> 1, 1, 4, 1, 1, 0, 7, 0, 5, 1, 0, 6, 1, 10, 11, 9,~
$ TUKANG <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0~
$ TUKANG_1 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ TUKANG_12 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ TUKANG__13 <dbl> 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0~
$ TUKANG__14 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ TUKANG__15 <dbl> 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0~
$ TUKANG__16 <dbl> 7, 4, 10, 14, 0, 0, 2, 0, 7, 8, 0, 8, 1, 0, 3, 2,~
$ TUKANG__17 <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0~
$ PENATA <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0~
$ PENATA_ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PENATA1_1 <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 2, 0, 1, 0, 0, 0~
$ MEKANIK <dbl> 11, 1, 10, 8, 0, 0, 4, 0, 7, 8, 0, 9, 0, 15, 10, ~
$ SENIMAN_ <dbl> 4, 0, 12, 28, 0, 0, 2, 0, 3, 4, 0, 9, 6, 7, 14, 1~
$ TABIB <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0~
$ PARAJI_ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PERANCA_ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 1, 0, 1~
$ PENTER_ <dbl> 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2~
$ IMAM_M <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PENDETA <dbl> 2, 4, 5, 33, 0, 0, 20, 0, 10, 8, 0, 30, 14, 14, 1~
$ PASTOR <dbl> 0, 1, 0, 1, 0, 0, 8, 0, 0, 0, 0, 23, 0, 0, 0, 0, ~
$ WARTAWAN <dbl> 7, 1, 16, 27, 0, 0, 4, 0, 8, 6, 0, 9, 5, 9, 26, 3~
$ USTADZ <dbl> 6, 1, 1, 5, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0~
$ JURU_M <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0~
$ PROMOT <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ ANGGOTA_ <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 2, 1, 2~
$ ANGGOTA1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0~
$ ANGGOTA1_1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PRESIDEN <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ WAKIL_PRES <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1~
$ ANGGOTA1_2 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0~
$ ANGGOTA1_3 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0~
$ DUTA_B <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1~
$ GUBERNUR <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1~
$ WAKIL_GUBE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ BUPATI <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ WAKIL_BUPA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ WALIKOTA <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ WAKIL_WALI <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ ANGGOTA1_4 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1~
$ ANGGOTA1_5 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ DOSEN <dbl> 3, 2, 23, 36, 1, 2, 11, 0, 3, 5, 0, 14, 6, 28, 69~
$ GURU <dbl> 72, 40, 272, 378, 118, 72, 69, 116, 126, 71, 36, ~
$ PILOT <dbl> 1, 0, 2, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 3, 1~
$ PENGACARA_ <dbl> 4, 1, 8, 22, 0, 0, 5, 0, 5, 4, 0, 4, 3, 12, 24, 2~
$ NOTARIS <dbl> 0, 0, 3, 5, 0, 0, 4, 0, 0, 0, 0, 5, 0, 5, 10, 3, ~
$ ARSITEK <dbl> 1, 0, 2, 3, 0, 0, 2, 0, 0, 0, 0, 4, 1, 2, 7, 3, 9~
$ AKUNTA_ <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 1, 0, 0, 2~
$ KONSUL_ <dbl> 1, 0, 2, 11, 0, 0, 4, 0, 0, 0, 0, 6, 2, 3, 10, 8,~
$ DOKTER <dbl> 16, 32, 35, 68, 0, 1, 63, 0, 27, 32, 1, 63, 48, 6~
$ BIDAN <dbl> 3, 1, 9, 18, 12, 8, 1, 3, 3, 3, 7, 3, 10, 10, 7, ~
$ PERAWAT <dbl> 7, 0, 25, 44, 12, 10, 3, 6, 12, 20, 6, 7, 26, 16,~
$ APOTEK_ <dbl> 0, 0, 2, 3, 1, 0, 0, 0, 1, 2, 0, 1, 2, 3, 3, 3, 3~
$ PSIKIATER <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 3, 2, 1~
$ PENYIA_ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0~
$ PENYIA1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PELAUT <dbl> 0, 0, 6, 16, 1, 1, 0, 14, 2, 4, 1, 2, 4, 2, 10, 1~
$ PENELITI <dbl> 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1~
$ SOPIR <dbl> 65, 3, 94, 123, 0, 1, 61, 0, 76, 79, 0, 63, 44, 1~
$ PIALAN <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ PARANORMAL <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0~
$ PEDAGA_ <dbl> 379, 126, 321, 562, 11, 10, 412, 15, 202, 225, 0,~
$ PERANG_ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ KEPALA_ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ BIARAW_ <dbl> 0, 1, 0, 0, 0, 0, 22, 0, 3, 0, 0, 2, 1, 0, 4, 0, ~
$ WIRASWAST_ <dbl> 1370, 611, 1723, 3099, 131, 119, 1128, 259, 2321,~
$ LAINNYA_12 <dbl> 94, 57, 82, 122, 12, 10, 41, 6, 89, 158, 24, 37, ~
$ LUAS_DESA <dbl> 25476, 25477, 25396, 25399, 25377, 25378, 25389, ~
$ KODE_DES_3 <chr> "3173031006", "3173031007", "3171031003", "317103~
$ DESA_KEL_1 <chr> "KEAGUNGAN", "GLODOK", "HARAPAN MULIA", "CEMPAKA ~
$ KODE_12 <dbl> 317303, 317303, 317103, 317103, 310101, 310101, 3~
$ geometry <MULTIPOLYGON [m]> MULTIPOLYGON (((-3626874 69..., MULT~Finding the common values of outer islands. By clicking at a few of the outer islands, we can see that one common data value is “KEPULAUAN SERIBU” under “KAB_KOTA”(City District) attribute.
[1] 2Using the colSums to check the number of missing values in each attribute. From this we can see that there are at most 2 unique missing value. We need to check if we can remove the 2 value or we should keep it.
OBJECT_ID KODE_DESA DESA KODE PROVINSI KAB_KOTA
0 0 0 0 0 2
KECAMATAN DESA_KELUR JUMLAH_PEN JUMLAH_KK LUAS_WILAY KEPADATAN
2 2 0 0 0 0
PERPINDAHA JUMLAH_MEN PERUBAHAN WAJIB_KTP SILAM KRISTEN
0 0 0 0 0 0
KHATOLIK HINDU BUDHA KONGHUCU KEPERCAYAA PRIA
0 0 0 0 0 0
WANITA BELUM_KAWI KAWIN CERAI_HIDU CERAI_MATI U0
0 0 0 0 0 0
U5 U10 U15 U20 U25 U30
0 0 0 0 0 0
U35 U40 U45 U50 U55 U60
0 0 0 0 0 0
U65 U70 U75 TIDAK_BELU BELUM_TAMA TAMAT_SD
0 0 0 0 0 0
SLTP SLTA DIPLOMA_I DIPLOMA_II DIPLOMA_IV STRATA_II
0 0 0 0 0 0
STRATA_III BELUM_TIDA APARATUR_P TENAGA_PEN WIRASWASTA PERTANIAN
0 0 0 0 0 0
NELAYAN AGAMA_DAN PELAJAR_MA TENAGA_KES PENSIUNAN LAINNYA
0 0 0 0 0 0
GENERATED KODE_DES_1 BELUM_ MENGUR_ PELAJAR_ PENSIUNA_1
2 2 0 0 0 0
PEGAWAI_ TENTARA KEPOLISIAN PERDAG_ PETANI PETERN_
0 0 0 0 0 0
NELAYAN_1 INDUSTR_ KONSTR_ TRANSP_ KARYAW_ KARYAW1
0 0 0 0 0 0
KARYAW1_1 KARYAW1_12 BURUH BURUH_ BURUH1 BURUH1_1
0 0 0 0 0 0
PEMBANT_ TUKANG TUKANG_1 TUKANG_12 TUKANG__13 TUKANG__14
0 0 0 0 0 0
TUKANG__15 TUKANG__16 TUKANG__17 PENATA PENATA_ PENATA1_1
0 0 0 0 0 0
MEKANIK SENIMAN_ TABIB PARAJI_ PERANCA_ PENTER_
0 0 0 0 0 0
IMAM_M PENDETA PASTOR WARTAWAN USTADZ JURU_M
0 0 0 0 0 0
PROMOT ANGGOTA_ ANGGOTA1 ANGGOTA1_1 PRESIDEN WAKIL_PRES
0 0 0 0 0 0
ANGGOTA1_2 ANGGOTA1_3 DUTA_B GUBERNUR WAKIL_GUBE BUPATI
0 0 0 0 0 0
WAKIL_BUPA WALIKOTA WAKIL_WALI ANGGOTA1_4 ANGGOTA1_5 DOSEN
0 0 0 0 0 0
GURU PILOT PENGACARA_ NOTARIS ARSITEK AKUNTA_
0 0 0 0 0 0
KONSUL_ DOKTER BIDAN PERAWAT APOTEK_ PSIKIATER
0 0 0 0 0 0
PENYIA_ PENYIA1 PELAUT PENELITI SOPIR PIALAN
0 0 0 0 0 0
PARANORMAL PEDAGA_ PERANG_ KEPALA_ BIARAW_ WIRASWAST_
0 0 0 0 0 0
LAINNYA_12 LUAS_DESA KODE_DES_3 DESA_KEL_1 KODE_12 geometry
0 0 2 2 0 0 Using rowSums and extracting any rows with missing value, we can see that it is truly 2 unique row that contains missing values in some of the columns, however, the data can still be kept within the dataframe as it provides geospatial properties that is part of a full constructed map.
Simple feature collection with 2 features and 161 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -3623599 ymin: 691982.5 xmax: -3620985 ymax: 693163.1
Projected CRS: DGN95 / Indonesia TM-3 zone 54.1
OBJECT_ID KODE_DESA DESA KODE PROVINSI KAB_KOTA
243 25645 31888888 DANAU SUNTER 318888 DKI JAKARTA <NA>
244 25646 31888888 DANAU SUNTER DLL 318888 DKI JAKARTA <NA>
KECAMATAN DESA_KELUR JUMLAH_PEN JUMLAH_KK LUAS_WILAY KEPADATAN
243 <NA> <NA> 0 0 0 0
244 <NA> <NA> 0 0 0 0
PERPINDAHA JUMLAH_MEN PERUBAHAN WAJIB_KTP SILAM KRISTEN KHATOLIK
243 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0
HINDU BUDHA KONGHUCU KEPERCAYAA PRIA WANITA BELUM_KAWI KAWIN
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
CERAI_HIDU CERAI_MATI U0 U5 U10 U15 U20 U25 U30 U35 U40 U45 U50
243 0 0 0 0 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0 0 0 0 0
U55 U60 U65 U70 U75 TIDAK_BELU BELUM_TAMA TAMAT_SD SLTP SLTA
243 0 0 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0 0 0
DIPLOMA_I DIPLOMA_II DIPLOMA_IV STRATA_II STRATA_III BELUM_TIDA
243 0 0 0 0 0 0
244 0 0 0 0 0 0
APARATUR_P TENAGA_PEN WIRASWASTA PERTANIAN NELAYAN AGAMA_DAN
243 0 0 0 0 0 0
244 0 0 0 0 0 0
PELAJAR_MA TENAGA_KES PENSIUNAN LAINNYA GENERATED KODE_DES_1
243 0 0 0 0 <NA> <NA>
244 0 0 0 0 <NA> <NA>
BELUM_ MENGUR_ PELAJAR_ PENSIUNA_1 PEGAWAI_ TENTARA KEPOLISIAN
243 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0
PERDAG_ PETANI PETERN_ NELAYAN_1 INDUSTR_ KONSTR_ TRANSP_ KARYAW_
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
KARYAW1 KARYAW1_1 KARYAW1_12 BURUH BURUH_ BURUH1 BURUH1_1
243 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0
PEMBANT_ TUKANG TUKANG_1 TUKANG_12 TUKANG__13 TUKANG__14
243 0 0 0 0 0 0
244 0 0 0 0 0 0
TUKANG__15 TUKANG__16 TUKANG__17 PENATA PENATA_ PENATA1_1 MEKANIK
243 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0
SENIMAN_ TABIB PARAJI_ PERANCA_ PENTER_ IMAM_M PENDETA PASTOR
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
WARTAWAN USTADZ JURU_M PROMOT ANGGOTA_ ANGGOTA1 ANGGOTA1_1
243 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0
PRESIDEN WAKIL_PRES ANGGOTA1_2 ANGGOTA1_3 DUTA_B GUBERNUR
243 0 0 0 0 0 0
244 0 0 0 0 0 0
WAKIL_GUBE BUPATI WAKIL_BUPA WALIKOTA WAKIL_WALI ANGGOTA1_4
243 0 0 0 0 0 0
244 0 0 0 0 0 0
ANGGOTA1_5 DOSEN GURU PILOT PENGACARA_ NOTARIS ARSITEK AKUNTA_
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
KONSUL_ DOKTER BIDAN PERAWAT APOTEK_ PSIKIATER PENYIA_ PENYIA1
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
PELAUT PENELITI SOPIR PIALAN PARANORMAL PEDAGA_ PERANG_ KEPALA_
243 0 0 0 0 0 0 0 0
244 0 0 0 0 0 0 0 0
BIARAW_ WIRASWAST_ LAINNYA_12 LUAS_DESA KODE_DES_3 DESA_KEL_1
243 0 0 0 0 <NA> <NA>
244 0 0 0 0 <NA> <NA>
KODE_12 geometry
243 0 MULTIPOLYGON (((-3620985 69...
244 0 MULTIPOLYGON (((-3622382 69...[1] 0